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Lightdash supports dbt v1.4.0 and above. If you are using an older version of dbt, you will need to upgrade to sync your project to Lightdash

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Syncing your dbt project to Lightdash

You can sync your dbt project code with Lightdash in a few different ways. We recommend everyone set up continuous deployment, but you can also refresh in the Lightdash app or deploy from the CLI.

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1. Set up continous deployment

Read how to do that and check out our example workflow files.

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2. Click “Refresh dbt” in Lightdash

The button can be found on the Query from tables page.
screenshot-refresh-dbt
If you’re using a git connection (like GitHub, Gitlab or Bitbucket), you’ll need to push + merge your changes to the branch that your Lightdash project is connected to before you press Refresh dbt.
If you’ve made any changes to the underlying data (for example, adding a new column in your model.sql file or changing the SQL logic of a dimension), then you need to run: dbt run -m yourmodel before you click Refresh dbt in Lightdash.

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3. Push code from the CLI

If you’re using the Lightdash CLI, you can use lightdash deploy to deploy your changes to Lightdash. Read more about how to use lightdash deploy.
We don’t recommend using lightdash deploy from your local environment as the primary way you update Lightdash since small mistakes can lead to production issues.

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dbt project settings

For more information about dbt connection types (Github, Gitlab, Bitbucket, etc.) and the fields required for each type, read the dbt project section in our connection guide. Below are details about the univeral fields for all connected dbt projects.

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Target name

Target contains information about your dbt connection to your warehouse. It’s the dataset or schema in your data warehouse that Lightdash will look for your dbt models. By default, we set this to be the same value as you have as the default in your profiles.yml file when you run lightdash deploy (if that’s how you created or recently deployed your project). If you want to update this, you can enter the target of your choice in the project settings (for example prod or analytics.) Read more about dbt targets in the dbt docs.

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dbt selector

This setting is found under Advanced configuration options
You can filter out models in your dbt project that you don’t want to see in Lightdash. This is useful if you have a large dbt project and you want to speed up the sync process. Unlike table selection, this selector is applied to the dbt models, so it will skip the entire compilation process for the models that you don’t want to see in Lightdash. To do this, you can add a dbt selector to your project settings. This is a JSON object that contains the models you want to include in Lightdash. For example, if you only want to include the my_model and all models with the lightdash tag in Lightdash, you can add the following to your dbt project settings: 
my_model tag:lightdash
We support all dbt selectors. Read more about selectors in the dbt docs.

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Environment variables

This setting is found under Advanced configuration options
If your dbt project uses environment variables in your dbt profiles.yml file, you can add these to Lightdash here. For each environment variable, you’ll need to add the key + value pair for the item. You’ll normally find these values in a file called .env in your dbt project directory. For example, I might have something like: 
profile:
  target: prod
  outputs:
    prod:
      type: postgres
      host: 127.0.0.1
      user: "{{ env_var('DBT_USER') }}"
      ....
Then a .env file like: 
export DBT_USER="myspecialuserkey123"
So, in Lightdash, I’d add a new environment variable and put key as DBT_USER and value as myspecialuserkey123.
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